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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(1): 57-62 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.386

Skin Segmentation Using GMM Classifier and Texture Feature Extraction

Chi-Man Pun and Pan Ng
Abstract—In this paper, a skin color segmentation approach by texture feature extraction and k-mean clustering is proposed. We improved the traditional skin classification by combining both color and texture features for skin segmentation. After the color segmentation using a 16 – GMM (Gaussian Mixture Models) classifier, the texture features are extracted using effective wavelet transform with a 2-D Daubechies Wavelet and represented as a list of Shannon entropy. The non-skin regions can be eliminated by the Skin Texture-cluster Elimination using K-mean clustering. Experimental results based on common datasets show that our proposed can achieve better performance compared to the existing methods with true positive of 96.5% and with false positives 25.2% for the worst case, with true positive of 90.3% and with false positives 20.5% for the normal case.

Index Terms—Skin segmentation, texture feature, wavelet transform, k-mean clustering.

C.-M. Pun and P. Ng are with the Department of Computer and Information Science, University of Macau , Macau S.A.R., China (e-mail: cmpun@umac.mo).


Cite:Chi-Man Pun and Pan Ng, "Skin Segmentation Using GMM Classifier and Texture Feature Extraction," International Journal of Machine Learning and Computing vol.4, no. 1, pp. 57-62, 2014.

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